Method of and system for displaying changes in a medical state of a patient with machine learning

ABSTRACT

A method, system, and program applies machine learning in displaying a patient&#39;s medical state. A first set of data can be received corresponding to a medical state of the patient and defined by a first plurality of positives corresponding to deviations from a healthy state. The first positives are grouped to a second plurality of positives defining by a medical condition. Individual first positives can correlate to a different medical conditions, thus defining a plurality a problem bundles associated with the patient. A mismatch occurs when one of the first positives is not groupable to the second positives of a particular condition. Via machine learning, mismatches are reduced by repeatedly identifying the mismatch and revising a second data set of the relevant medical condition to include the relevant first positive, eliminating the mismatch from a subsequent groupings and displays.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of application Ser. No.17/391,672 for a METHOD OF AND SYSTEM FOR MANAGING AN ELECTRONIC HEALTHRECORD AND DISPLAYING A MEDICAL STATE OF A PATIENT, filed on Aug. 3,2021, which was a continuation application of application Ser. No.15/135,835 for a METHOD OF AND SYSTEM FOR MANAGING AN ELECTRONIC HEALTHRECORD AND DISPLAYING A MEDICAL STATE OF A PATIENT, filed on Apr. 22,2016, which claimed the benefit of U.S. Provisional Patent ApplicationSer. No. 62/150,883 for a SYSTEM FOR THE GENERATION AND MANAGEMENT OFELECTRONIC HEALTH RECORDS, filed on 2015 Apr. 22. The presentapplication claims priority to all of these applications and all arehereby incorporated by reference in their entireties.

BACKGROUND 1. Field

The present disclosure relates to electronic health record systems, andmore particularly relates to graphical displays with which display thephysical condition of the patient.

2. Description of Related Prior Art

U.S. Pat. No. 8,792,968 discloses a system and method for healthevaluation. The system is an apparatus and a method for human healthevaluation utilizing Thermal Micro Texture mapping technology. Themethod comprises scanning body areas of a patient utilizing an infraredcamera, detecting abnormalities in the body of the patient, analyzingabnormalities of the patient against information stored in a database,and reporting results to the patient in a pre-determined format. Themethod provides an earlier discovery of disease by mapping and analyzingabnormal temperatures changes in the body, which can help prevent thedisease from progressing at an early stage.

U.S. Pat. No. 9,122,776 discloses an ENHANCED ELECTRONIC HEALTH RECORDGRAPHICAL USER INTERFACE SYSTEM. A user device having a display accesseselectronic health records and clinic note templates stored on digitalstorage segments. A template selection screen is presented on thedisplay of the user device. The template selection screen has at leasttwo view modes. One view mode is a grid view, in which iconrepresentations of various clinic note templates are displayed, eachicon representation having a number of secondary icons providingadditional functionality and information to the user. Also available isa list view, which also contains a vertical listing of available clinicnote templates, each list element also having secondary icons. Uponselection of a template, the user is presented with a formatted clinicnote. Additional functionality is available to the user to aid in theefficient capture of information.

U.S. Pat. No. 9,298,182 discloses a METHOD OF DISPLAYING ABNORMAL STATUSIN PLANT OPERATION MONITORING SYSTEM. The disclosure pertains to amethod of displaying an abnormal status in a plant operation monitoringsystem. The method of displaying the abnormal status includes the stepsof: receiving information of operational states from a sensor mounted ondevices, machines and facilities constituting an industrial plantsystem; checking any abnormal signal in the operational states,logically grouping parts influencing on operational values when theabnormal signal is generated, and displaying the parts on a piping &instrument drawing (P&ID) on a monitor; and tracing the abnormal signalin a reverse direction of a system flow, searching out the device whichcauses the abnormal state, and displaying the abnormal device on thepiping & instrument drawing.

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

SUMMARY

A method, a system for executing the method, and a computer programproduct comprising program code by which machine learning is applied indisplaying a patient's medical state is disclosed. The method caninclude receiving, at a computing device having one or more processors,a first input of a first set of data corresponding to a first medicalstate of the patient and including a first plurality of positives. Eachof the first plurality of positives can correspond to a deviation from ahealthy state for a particular anatomical portion of the patient's bodyand is a numerical value. The method can also include grouping, at thecomputing device, at least some of the first plurality of positives to asecond plurality of positives, wherein a plurality of different medicalconditions are each defined by a respective second data set of one ofmore the second plurality of positives. Also, the at least some of thefirst plurality of positives can collectively correlate to at least oneof the plurality of different medical conditions and the respectivesecond plurality of positives, thus defining a first plurality a problembundles associated with the patient. Each problem bundle can be at leastone of the first plurality of positives of the first set of data groupedto at least one of the second plurality of positives of the second setof data of a particular medical condition. The method can also includeidentifying, at the computing device, at least one mismatch with thefirst plurality of problem bundles associated with the patient duringsaid grouping. The at least one mismatch can be at least one of thefirst plurality of positives of the patient that is not groupable to oneof the second plurality of positives of at least one particular medicalcondition. The at least one of the first plurality of positives can thusbe in the first set of data and not in the second set of data of the atleast one particular medical condition. The method can also includedisplaying, on a display controlled by the computing device, a firststatgraph that includes a plurality of objects including a firstplurality of nodes each corresponding to one of the first plurality ofpositives. The method can also include reducing mismatches, at thecomputing device, via a machine learning algorithm, including repeatedlyidentifying, at the computing device, a first mismatch between a firstpositive and a first problem bundle involving a first medical condition.The reducing action can also include revising, at the computing device,in response to said repeatedly identifying, a second data set of thefirst medical condition to include the first positive and therebyeliminate the first mismatch from a subsequent grouping involving thefirst medical condition.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description set forth below references the followingdrawings:

FIG. 1 is a perspective view of a patient interacting with a health careprovider according to an exemplary embodiment of the present disclosure;

FIG. 1A is a perspective view of a first component of an exemplarycomputing device according to some implementations of the presentdisclosure;

FIG. 2 is a functional block diagram of the first component of anexemplary computing device according to some implementations of thepresent disclosure;

FIG. 3 is a perspective view of a second component of an exemplarycomputing device according to some implementations of the presentdisclosure;

FIG. 4 is a functional block diagram of the second component of anexemplary computing device according to some implementations of thepresent disclosure;

FIG. 5 is a perspective view of a retaining station for a plurality ofsecond components;

FIG. 6 is a front view of a third component of an exemplary computingdevice according to some implementations of the present disclosure;

FIG. 7 is a back view of the third component of an exemplary computingdevice according to some implementations of the present disclosure;

FIG. 8 is a side view of the third component of an exemplary computingdevice according to some implementations of the present disclosure;

FIG. 9 is a functional block diagram of the third component of anexemplary computing device according to some implementations of thepresent disclosure;

FIG. 10 is a perspective view of a carousel for a plurality of thirdcomponents;

FIG. 11 is a perspective view of a third component positioned in adocking station outside of an examination room;

FIG. 12 is a diagram of a computing system including an exemplarycomputing device according to some implementations of the presentdisclosure;

FIG. 13 is a screen shot of an exemplary graphic user interfaceaccording to some implementations of the present disclosure;

FIG. 14 is a top-down view of plane divided into a grid of a pluralityof cells and wherein at least some of the plurality of cells correspondto the particular anatomical portions of the patient's body, wherein theplane is a two-dimensional medical statgraph for a system according toan exemplary embodiment of the present disclosure with a homunculus fordirect examination findings (hereafter DEF), pockets for reportedfunctional loss (hereafter RFL), and belts for sensorimotor anomaly(hereafter SMA), algia and trauma;

FIG. 15 is a perspective view of the plane shown in FIG. 14 , with nodesprojecting from the plane and axes extending between the nodes;

FIG. 16 is a magnified portion of FIG. 15 ;

FIG. 17 is a view of the objects shown in FIG. 16 as well as a planargrouping object; and

FIG. 18 is a side view of the objects shown in perspective view in FIG.17 .

DETAILED DESCRIPTION

The inventor has come to appreciate that a major objective in medicalrecord documentation is to demonstrate the level of “work” at eachpatient visit so that the efficacy of the medical services can beobjectively determined. This is not so difficult for discreteactivities, such as performance of surgical procedures, but becomes muchmore difficult when attempting to quantify cognitive effort on the partof the doctor during an office visit. The term “doctor” or “health careprovider” is used herein to apply to an individual providing evaluationand management services to a patient, not just actual doctors. By way ofexample and not limitation, an individual providing evaluation andmanagement services to a patient can be referred to as a provider, apractitioner, or a nurse.

A stereotypical perception exists that doctors have bad handwriting. Amajor cause of this often justified perception is that doctors have toexpress detailed assessments of large amounts of information in awritten form under pressure of time. Some of this is information is oflow value density (when density is expressed as unit of usefulinformation per text character) since it represents essentially apreamble, recap or confirmation of previously obtained information.Interspersed is information of high value density which isunintelligible without the accompanying low value textual information togive it context. Doctors tend to hurry through the formal, verbosesections of their written medical communications sacrificing legibilityfor speed to focus on what they perceive to be the more important andpertinent expressions. This causes a deterioration in handwriting,initially for the less value dense information but eventually alsoaffecting the sections with higher information value density. Manyattempts have been made to reduce this problem by adopting abbreviationsand code phrases for common medical concepts, terms, and linkages, butthe lack of standardization makes this approach risky and of limitedvalue. More recently, this problem has been tackled by the introductionof electronic health records (hereafter referred to as “EHR”individually) in an attempt to standardize medical notation. Althoughthe problem of illegible handwriting has been largely solved by textentry via keyboard, the current methodology is very burdensome becauseit increases time and effort required for data entry and makesnavigation through a computerized record much more difficult thanthrough a paper chart and causes important information to be diluted andlost within an engulfing sea of text, obscured by ebbing and flowingtides of tabs and windows across the impenetrable computer monitor.

The reality is that most current EHRs generate large volumes of textwith very low value density of information when the density of suchinformation is measured across comparable media of visual attention, forexample, a paper chart page versus a screenshot of an EHR window open ona monitor or tablet screen. In fact, because doctors have become trainedto look for certain penned notations, particularly in relation to theirpositioning or formatting within a paper chart, the bland presentationof text in an EHR actually hinders communication. The ability to easilymanipulate large sections of electronic text, such as by copying andpasting, also creates vulnerabilities to fraud, overutilization ofservices and repetition of medical errors.

The main purposes of a medical record, traditional or EHR, are to (1)record medical work both cognitive and physical, (2) order, direct andreport appropriate therapy at each patient encounter, and (3)communicate the details of the medical assessment of the patient as wellas the result of therapy to authorized parties. One or more embodimentsof the present disclosure can achieve these three objectives in aconsistent and effective manner. Such embodiments can do so by analyzinga change in the medical status of the patient rather than an absolutemeasure of the patient status to properly calculate the efficiency ofmedical work. The rate of change of the medical status that iscalculated can be applied to measure efficacy of treatment plans.

For such measures of change to be meaningful, the medical status of anyindividual needs to be described in terms whose magnitude can bequantified to a useful degree and so that the number of constituentelements contributing to these terms is small enough for easymathematical analysis yet large enough to encompass all the conceptsthat need to be expressed for practical purposes. Also the depiction ofthe medical status needs to be in a format so that common patterns ofdisease complexes are easily recognized by trained staff, and importantdeviations or anomalies are promptly communicated to the conscious andsub-conscious attention of treating doctors requiring the minimum visualeffort.

The constituent elements of the medical status of a patient are based onthe following “Descriptive Foundations:”

-   -   Subjective backed by objective—medical diagnoses pertaining to        self, cohabitants and genetic relatives as well as reported        exposure to trauma or disease agents;    -   Purely Subjective—complaints graded by the nature (location on        belts around homunculus) and the degree of suffering;    -   Objective backed by subjective—measurable elements of biological        functional loss; and    -   Purely Objective—doctor identified abnormalities on physical        examination listed by pathophysiological observed        pathophysiological changes at specific anatomical location.

At each patient visit, the computation of the doctor's work includesrecording what tasks and elements of the medical encounter wereperformed at each visit. The elements are traditionally grouped in tothree sections: (1) medical history taking (with various predefinedsub-components), (2) physical examination (again with sub-sectionsrelated to the specialty), and (3) clinical decision making (alsocomposed of sub-sections such as interpretation of diagnostic tests,ordering of tests, review of other records, communication with otherproviders and initiation of prescriptions, counseling etc). Existing EHRsystems create incentives and disincentives, wherein increasing levelsof data input in sections (1) and (2) are only rewarded if theunderlying problems addressed in item (3) exceed a certain threshold ofrisk or complexity. This approach is justified by the rationale thatexcessive questioning and examination would be wasteful for minorproblems. Similarly, existing systems will limit the number of timesthat conditions only recognized by higher levels (items (1) and (2))will be reimbursed within a given time period to discourage excessutilization. Such limits may be unfair if a doctor is treating apopulation with heavy disease burden and will discourage the doctor fromattending to such patients. Similarly, sometimes management of complexproblems is not reimbursed at a sufficient level simply because a doctordid not document completion of all the elements within the history andthe exam because these elements were not considered necessary due totime constraints. One example is a failure to record the social historyof a patient who is treated for an urgent injury.

Imperfections of existing systems are only partially recognized. Theinventor has also come to appreciate that accommodation or flexibilitygranted due to the imperfections of existing systems adds to subjectivegray areas ripe for abuse. Existing approaches to flexibility rewardsthose who focus energy on “gaming” the system rather than on patientcare.

Most clinical work in the office setting is usually done in elucidatingthe patient's medical background at the initial visit. Subsequent visitscan include elucidating the patient's medical background when theinterval over which care is given is relatively long. However, when thepatient is seen frequently and in quick succession for a number ofoverlapping or related problems, it becomes difficult to establish howmuch new or useful clinical work is done at each visit. Traditionally, alengthy handwritten note implied that more work had been done. However,a note generated by computer from information entered at an earliervisit, could be copied and pasted into a subsequent encounter by anartful scribe willing to gamble that nothing significant had changedother than the fluid elements of the active problem, and would withstandscrutiny most of the time. Conversely, a skilled clinician can quicklyscan several elements of the medical status and hone in on relevantitems for deeper inquiry on follow-up visits, and thus perform valuableclinical work with efficiency without necessarily enumerating each itemor having their acumen recognized.

The advent of EHRs has thus potentially facilitated overpayment ofservices for those providers adept at efficiently marking “check boxes”and carrying forward verbose text descriptions rather than thoseexercising clinical effort accurately. This problem exists becausecurrent EHRs are not usually “smart” in that they do not recognize thenatural history and chronological evolution of most common medicalconditions but instead record a static snapshot, repainted at each visitby rote process.

The present disclosure addresses the multi-layered shortcomings in theart that has been described above. The present disclosure provides asystem for the generation and management of electronic health recordswherein human disease complexes are described as related groups ofpositives in the Descriptive Foundations elucidated during theconstruction of the medical status (creation of the “medicalstatgraph”). As used herein, a “positive” is an elucidated particularpatient reported condition or finding that is a deviation from abaseline healthy condition. The positives in the Descriptive Foundationsare graphically depicted as nodes in a grid having a plurality ofdimensions and representing medical locations both in the anatomical andabstract (sensory and functional) sense. The grid is composed around aminiature human figure (termed a homunculus) and is infinitelyadjustable as three-dimensional structure models are adjustable (zoom,pan, rotate, etc) to any desired level of detail or specificity whichwill vary from one specialty to another. The homunculus and itssatellite regions provide all the location data needed to identify anypositives in the Descriptive Foundations. Construction of the statgraphwith its attendant positives in the Descriptive Foundations isequivalent to the history and examination portions of the traditionalmedical consultation (Subjective and Objective parts of a SubjectiveObjective Assessment Plan or “SOAP note”). The “diagnosis” element ofthe medical consultation (the Assessment part of the SOAP note) consistsof the doctor inferring and declaring etiological associations betweenthe nodes identified as positives on the statgraph to derive “problembundles.” Problem bundles represent the constellation of subjective andobjective findings in the proper relative proportion that is typicallyfound in a disease complex of a given severity. A library of standardproblem bundles can be formulated from typical disease findings in thegeneral population and can be used to suggest pre-configured problembundles which may best fit the statgraph under consideration so that thedoctor can select these options to remove as many unlinked nodes aspossible until those remaining are dismissed as random “noise” orflagged for later investigation until all nodes are reconciled. Eachproblem bundle of a certain severity or acuity will have a holistictherapy plan associated with it consisting of all necessary elementssuch as education, medication, medical devices, surgical procedures,additional diagnostic tests, outside consultations and follow upappointments. Each section of such therapy plans will have tiers oftreatments intensity guided by current best practices and allowingalternatives in special cases of patient idiosyncrasy without the needto escalate to a whole higher severity level of therapy plan (forexample, alternative medications for those who happen to be allergic tothe “first line” medication). The activation of the therapy plan and itsconstituent tasks corresponds to the Plan section of the traditionalSOAP note. Computerized order entry is actuated so that the tasks areactivated automatically: e.g. printing of instructions, email link topatient portal, e-prescriptions and lab orders generated, fax sent toreferring physician, codes and notes sent to insurance carrier, recallreminder sent to calendar etc.

The respective severities of the conditions can be displayed asinterconnected bundles. By translating the traditional work steps(history, exam and decision making) into the construction and themonitoring of one or more problem bundles, a more accurate indication ofwork performed is provided. A problem bundle is a graphical depictionreflecting the patient condition and changes in the display of theproblem bundle demonstrate work done to improve the patient's health(rather than game the system). The work done will not be measured byitems that can be copied and pasted but by steps which confirm thepresence of the problem bundles and describe their dynamic change fromthe previous assessment. Therefore useful information will be capturedand retained at the initial/earlier visit and this work will berecognized, but there will be no reward for copying and pasting thisinformation into subsequent visits because the work to be recognizedwill be based on and calculated from the severity and number of problembundles being addressed. The problem bundles once recognized will haveto be actively addressed rather than just acknowledged at subsequentvisits because they will have an associated therapy plan that has to bemodulated. There will be no reward for simply listing a large number ofoverlapping and potentially duplicative items because the items willhave to be assigned to problem bundles if they are to be recognized aspart of clinical work. This system rewards coherent analysis anddecision making rather than just lengthy descriptions, as hastraditionally been the case.

A medical statgraph can be generated by one or more embodiments of thepresent disclosure. The nature and physical appearance of the medicalstatgraph will be explained in greater detail below. The medicalstatgraph can consist of a chronological series of evolving problembundles juxtaposed over a pictorial representation of the human body andits biological systems with problem bundles representing disease processcomposed of nodes and interconnected lines. The nodes describe natureand severity of medical findings and the lines represent presumedetiological associations. Problem bundles are fashioned and processed toconform to established constellations of medical symptoms, signs andobservations.

A medical statgraph can define a framework for representing the lifespan of a single patient on which problem bundles are pictoriallylocated. The statgraph is the medical “canvas” of an individual patienttaking into account their age and demographics including past diagnosesand treatments. The statgraph can serve as a backdrop to display thechronological evolution of problem bundles under the influence oftherapy plans. A new or current statgraph can be generated at theconclusion of each medical encounter by morphing of the statgraph at thelast visit. The degree of morphing can be used to calculate the medicalwork done at that encounter. The nature of the morphing can informclinical decision making. Projected statgraphs can be generated at thestart of a therapeutic plan. The variation from the projected statgraphto an actual statgraph can be used to determine whether therapy planneeds to be altered (escalated, continued, tapered or reconsidered incase of diagnostic confusion).

In an exemplary operating environment for one or more embodiments of thepresent disclosure, a patient or referring provider can contact a doctorvia phone, mail, fax or web to schedule consultation. Contact with thepatient can be attempted prior to the patient visit in order to obtainadvanced information regarding health issues and other relevantinformation. A patient record can be created from demographics and/orinformation can be imported through an electronic practice managementsystem (hereafter “EPM”). The patient's visit can be scheduled in theEPM.

Additional steps can be required for patients with existing paperrecords who are being seen for the first time after the introduction ofelectronic medical records. Staff can review, translate and paraphrasepast medical history prior to scheduled office visit to create a“current” or “presumed” medical statgraph.

The EHR is modified when a patient visits the doctor's office. After thepatient greeted, the patient can be logged in or signed into the EPMand/or EHR. The patient's identity can be verified. The patient can havea unique identifying number and/or photographs can be taken foridentification purposes. Other outstanding/additional documentary stepscan also be completed, such referrals, preauthorization, past medicalrecords, and previous and current prescriptions. A co-payment can becollected if sum is known in advance.

The patient can be given a welcome package and guided to a receptionarea. At this point, one or more embodiments of the present disclosurecan include requiring the patient to wear an identifying component (alsoreferred to herein as a “first component”. The component can be linkedto an identifying number associated with the patient as the patientmoves from clinical station to station. The component can take the formof a lapel badge; wristband; restaurant type pager; a two-piece, pairedheadset or earphone/headband; an ear piece; a pair of ear buds extendingfrom a tag; or smart glasses. The patient can be given the firstcomponent and placed in a row or file along with other patientsaccording to their turn.

FIG. 1 is a perspective view of an operating embodiment for a computingdevice 10 according to an exemplary embodiment of the presentdisclosure. It should be appreciated that a computing device 10according to one or more implementations of the present disclosure canbe cooperatively defined by structures that are physically remote fromone another, such, for example, a server and a tablet, or a tablet and astylus, or a patient wearable device and a server. Examples of computingdevices or portions of computing devices can include desktop computers,laptop computers, tablet computers, mobile phones, and smarttelevisions.

In FIG. 1 , a patient 12 is interacting with a healthcare provider 14according to an exemplary embodiment of the present disclosure in anexamination room 16. The patient 12 is wearing a first component 18 ofthe computing device 10. The first component 18 can be a device wearableby the patient 12. The healthcare provider 14 can be using a secondcomponent 20 and a third component 22 of the computing device 10. Theexemplary second component 20 can be shaped as a stylus. The thirdcomponent 22 can be defined by a tablet computer. A fourth component 24of the computing device 10 can be a server. The fourth component 24 canbe positioned in the same building as the examination room 16 or remotefrom the building containing the examination room 16. All of thecomponents 18, 20, 22 can be communicating data about the patient'smedical state to the component 24 for storage and processing.

FIG. 2 is a functional block diagram of the first component 18 of anexemplary computing device 10 according to some implementations of thepresent disclosure. The first component 18 can take the form of aheadset as shown in FIG. 1A. The first component 18 can be used tosummon the patient, confidentially communicate information, and tobroadcast audio recordings tailored for the patient. The first component18 can include a processor 26, memory 28, transceiver or communicationdevice 30, a position sensor 32, an outlet port 34, speaker 36, camera38, and a microphone 40. The first component 18 can also include abattery 25 providing power to the other modules of the first component18.

The processor 26 can be configured to control operation of the firstcomponent 18 of the computing device 10. It should be appreciated thatthe term “processor” as used herein can refer to both a single processorand two or more processors operating in a parallel or distributedarchitecture. The processor 26 can operate under the control of anoperating system, kernel and/or firmware and can execute or otherwiserely upon various computer software applications, components, programs,objects, modules, data structures, etc. Moreover, various applications,components, programs, objects, modules, etc. may also execute on one ormore processors in another computing device coupled to processor 26,e.g., in a distributed or client-server computing environment, wherebythe processing required to implement the functions of embodiments of thepresent disclosure may be allocated to multiple computers over anetwork. The processor 26 can be configured to perform general functionsincluding, but not limited to, loading/executing an operating system ofthe computing device 10, controlling communication via the communicationdevice 30, and controlling read/write operations at the memory 28. Theprocessor 26 can also be configured to perform specific functionsrelating to at least a portion of the present disclosure including, butnot limited to, collecting and reporting patient-related health data,facilitating communication between the patient 12 and the healthcareprovider 14, reporting the position of the patient 12 in the healthcarefacility.

Memory 28 can be defined in various ways in implementations of thepresent disclosure. Memory 28 can include computer readable storagemedia and communication media. Memory 28 can be non-transitory innature, and may include volatile and non-volatile, and removable andnon-removable media implemented in any method or technology for storageof information, such as computer-readable instructions, data structures,program modules or other data. Memory 28 can further include RAM, ROM,erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), flash memory or other solidstate memory technology, CD-ROM, digital versatile disks (DVD), or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to store the desired information and which can be accessed bythe processor 26. Memory 28 can store computer readable instructions,data structures or other program modules. By way of example, and notlimitation, communication media may include wired media such as a wirednetwork or direct-wired connection, and wireless media such as acoustic,RF, infrared and other wireless media. Combinations of any of the abovemay also be included within the scope of computer readable media.

The transceiver or communication device 30 is configured forcommunication between the processor 26 and other devices, e.g., thecomponent 24, via a network. The network can include a local areanetwork (LAN), a wide area network (WAN), e.g., the Internet, or acombination thereof. Specifically, the communication device 30 cantransmit and receive communications between the patient 12 and thehealthcare provider 14, video generated by the camera 38, and theposition of the patient 12 sensed by the position sensor 32.

The position sensor 32 can be configured to generate a position signalindicative of the position of the patient 12 within the healthcarefacility. The position sensor 32 can be configured to detect an absoluteor relative position of the patient 12 wearing the first component 18.The position sensor can transmit information regarding lateraljuxtaposition of anatomical sites in relation to diagnostic ortherapeutic instruments to reduce the risk of site errors, For example,to differentiate the left eye or ear from the right eye or ear. Theposition sensor 32 can electrically communicate a position signalcontaining position data to the processor 26 and the processor 26 cancontrol the communication device 30 to transmit the position signal tothe fourth component 24 through a network. Identifying the position ofthe patient 12 can be accomplished by radio, ultrasound or ultrasonic,infrared, or any combination thereof. The position sensor 32 can be acomponent of a real-time locating system (RTLS), which is used toidentify the location of objects and people in real time within abuilding such as a healthcare facility. The position sensor 32 caninclude a tag that communicates with fixed reference points in thehealthcare facility. The fixed reference points can receive wirelesssignals from the position sensor 32. The position signal can beprocessed to assist in determining one or more items that are proximateto the patient 12 and are visible in the video signal. The fourthcomponent 24 can receive position data, identify the location of thepatient 12, and communicate the position date the third component 22 insome embodiments of the present disclosure.

The outlet port 34 can allow the extraction of data stored in memory 28.The configuration of the outlet port 34 can be selected as desired. Byway of example and not limitation, the outlet port 34 can be a USB-A,USB-B, Mini-A & Mini-B, Micro-B, or Micro-AB.

The speaker 36 can be configured to emit sounds, messages, information,and any other audio signal to the patient 12. The speaker 36 can bepositioned within the range of hearing of the patient 12. Audio contenttransmitted by the fourth component 24 can be played for the patient 12through the speaker 36. The communication device 30 can receive theaudio signal from the fourth component 24 and direct the audio signal tothe processor 26. The processor 26 can then control the speaker 36 toemit the audio content.

The camera 38 can be configured to generate a video signal. The camera38 can be oriented to generate a video signal that approximates thefield of view of the patient 12 wearing the first component 18. Thecamera 38 can be operable to capture single images and/or video and togenerate a video signal based thereon.

The microphone 40 can be configured to generate an audio signal thatcorresponds to sound generated by and/or proximate to the patient 12.The audio signal can be processed by the processor 26 or by the fourthcomponent 24. Such audio signals can be correlated to the videorecording.

FIG. 3 is a perspective view of the second component 20 and FIG. 4 is afunctional block diagram of the second component 20. As shown in FIG. 3, the second component 20 can include a plurality of mechanical featuresincluding a lanyard aperture 42, a clip 44 for holding the secondcomponent 20 in a pocket, a rubber or elastomeric gripping surface 46, areceptacle 48 for receiving a finger, a tip 50 for supporting a writinginstrument or a rubber tip for engaging a touch screen, and one or morebuttons 78 engageable with a thumb or fingers. As shown in FIG. 3 , thesecond component 20 can include a plurality of electrical featuresincluding a processor 52, memory 54, a communication device 56, a lightemitting diode (LED) 58, a display 60, a microphone 62, a speaker 64, abiometric sensor 66, a radio frequency identification (RFID) chip 68, alaser 70, a camera 72, a proximity sensor 74, and an outlet port 76. Thesecond component 20 can also include a battery 78 providing power to theother modules of the second component 20.

The processor 52 can be configured to control operation of the secondcomponent 20 of the computing device 10. It should be appreciated thatthe term “processor” as used herein can refer to both a single processorand two or more processors operating in a parallel or distributedarchitecture. The processor 52 can operate under the control of anoperating system, kernel and/or firmware and can execute or otherwiserely upon various computer software applications, components, programs,objects, modules, data structures, etc. Moreover, various applications,components, programs, objects, modules, etc. may also execute on one ormore processors in another computing device coupled to processor 52,e.g., in a distributed or client-server computing environment, wherebythe processing required to implement the functions of embodiments of thepresent disclosure may be allocated to multiple computers over anetwork. The processor 52 can be configured to perform general functionsincluding, but not limited to, loading/executing an operating system ofthe computing device 10, controlling communication via the communicationdevice 56, and controlling read/write operations at the memory 54. Theprocessor 52 can also be configured to perform specific functionsrelating to at least a portion of the present disclosure including, butnot limited to, collecting and reporting patient-related health data.

Memory 54 can be defined in various ways in implementations of thepresent disclosure. Memory 54 can include computer readable storagemedia and communication media. Memory 54 can be non-transitory innature, and may include volatile and non-volatile, and removable andnon-removable media implemented in any method or technology for storageof information, such as computer-readable instructions, data structures,program modules or other data. Memory 54 can further include RAM, ROM,erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), flash memory or other solidstate memory technology, CD-ROM, digital versatile disks (DVD), or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to store the desired information and which can be accessed bythe processor 52. Memory 54 can store computer readable instructions,data structures or other program modules. By way of example, and notlimitation, communication media may include wired media such as a wirednetwork or direct-wired connection, and wireless media such as acoustic,RF, infrared and other wireless media. Combinations of any of the abovemay also be included within the scope of computer readable media.

The transceiver or communication device 56 is configured forcommunication between the processor 52 and other devices, e.g., thecomponent 24, via a network. The network can include a local areanetwork (LAN), a wide area network (WAN), e.g., the Internet, or acombination thereof. Specifically, the communication device 56 cantransmit and receive patient-related data associated with the medicalstate of the patient 12, from the second component 20 to the fourthcomponent 24 or the third component 22.

The LED 58 can be controlled by the processor 52 to emit light asdesired. Light can be desirable to illuminate a portion of the body ofthe patient 12. The display 60 can be configured to display videocontent. The display 60 can be configured to display text, graphics,images, illustrations and any other video signals to the patient 12 orthe healthcare provider 14. The microphone 62 can be configured togenerate an audio signal that corresponds to sound generated by and/orproximate to the patient 12, such as the voice of the patient 12 or thevoice of the healthcare provider 14. The audio signal can be processedby the processor 52 or by the fourth component 24. Such audio signalscan be correlated to any video recording. The speaker 64 can beconfigured to emit sounds, messages, information, and any other audiosignal to the patient 12 or the healthcare provider 14.

The biometric sensor 66 can be positioned at the bottom or innermostportion of the receptacle 48. The biometric sensor 66 can be utilized toverify the identity of the healthcare provider 14 or the patient 12. TheRFID chip 68 can be utilized to identify a particular second component20 from a plurality of similar second components. The laser 70 can beoperable to generate a laser beam that can be utilized during theexamination of the patient 12. The camera 72 can be configured togenerate a video signal. The camera 72 can be operable to capture singleimages and/or video and to generate a video signal based thereon. Thevideo signal can include images associated with the patient 12. Theoutlet port 76 can allow the extraction of data stored in memory 54. Theconfiguration of the outlet port 76 can be selected as desired. By wayof example and not limitation, the outlet port 76 can be a USB-A, USB-B,Mini-A & Mini-B, Micro-B, or Micro-AB.

FIG. 5 is a perspective view of a retaining station 80 for a pluralityof second components 20. Each of the second components 20 can be storedin a slot or holding pen of the retaining station 80. The retainingstation 80 can include one or more locking mechanisms to retain thesecond components 20. Each lock can be selectively unlocked through theuse of a biometric sensor, such as referenced at 82. Each healthcareprovider 14 can engage the particular biometric sensor to releasehis/her second component 20. The unlocking of the second component 20can also trigger the start of the work shift of the healthcare provider14, or the “clocking in” of the healthcare provider 14.

FIG. 6 is a front view of the third component 22, FIG. 7 is a back view,and FIG. 8 is a side view. FIG. 9 is a functional block diagram of thethird component 22. As shown in FIGS. 6-8 , the third component 22 caninclude a plurality of mechanical features including a handle 84, a clip86 for receiving a strap, one or more buttons 88 engageable with a thumbor fingers, and a receptacle 90 for receiving the second component 20.As shown in FIG. 9 , the third component 22 can include a plurality ofelectrical features including a processor 92, memory 94, a communicationdevice 96, a light emitting diode (LED) 98, a display 100, a microphone102, a speaker 104, a biometric sensor 106, cameras 108 and 110, and anoutlet port 112. The third component 22 can also include a battery 114providing power to the other modules of the third component 22.

The processor 92 can be configured to control operation of the thirdcomponent 22 of the computing device 10. It should be appreciated thatthe term “processor” as used herein can refer to both a single processorand two or more processors operating in a parallel or distributedarchitecture. The processor 92 can operate under the control of anoperating system, kernel and/or firmware and can execute or otherwiserely upon various computer software applications, components, programs,objects, modules, data structures, etc. Moreover, various applications,components, programs, objects, modules, etc. may also execute on one ormore processors in another computing device coupled to processor 92,e.g., in a distributed or client-server computing environment, wherebythe processing required to implement the functions of embodiments of thepresent disclosure may be allocated to multiple computers over anetwork. The processor 92 can be configured to perform general functionsincluding, but not limited to, loading/executing an operating system ofthe computing device 10, controlling communication via the communicationdevice 96, and controlling read/write operations at the memory 94. Theprocessor 92 can also be configured to perform specific functionsrelating to at least a portion of the present disclosure including, butnot limited to, collecting and reporting patient-related health data.

Memory 94 can be defined in various ways in implementations of thepresent disclosure. Memory 94 can include computer readable storagemedia and communication media. Memory 94 can be non-transitory innature, and may include volatile and non-volatile, and removable andnon-removable media implemented in any method or technology for storageof information, such as computer-readable instructions, data structures,program modules or other data. Memory 94 can further include RAM, ROM,erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), flash memory or other solidstate memory technology, CD-ROM, digital versatile disks (DVD), or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to store the desired information and which can be accessed bythe processor 92. Memory 94 can store computer readable instructions,data structures or other program modules. By way of example, and notlimitation, communication media may include wired media such as a wirednetwork or direct-wired connection, and wireless media such as acoustic,RF, infrared and other wireless media. Combinations of any of the abovemay also be included within the scope of computer readable media.

The transceiver or communication device 96 is configured forcommunication between the processor 92 and other devices, e.g., thecomponent 24, via a network. The network can include a local areanetwork (LAN), a wide area network (WAN), e.g., the Internet, or acombination thereof. Specifically, the communication device 96 cantransmit and receive patient-related data associated with the medicalstate of the patient 12, from the third component 22 to the fourthcomponent 24 or the second component 20.

The LED 98 can be controlled by the processor 92 to emit light asdesired. Light can be desirable to illuminate a portion of the body ofthe patient 12 or, as set forth below, to render the third component 22more easily visible. The display 100 can be configured to display videocontent. The display 100 can be configured to display text, graphics,images, illustrations and any other video signals to the patient 12 orthe healthcare provider 14. Data from the EHR of the patient 12 can bedisplayed on the display 100. A graphic user interface for enteringinput to the EHR of the patient 12 can be displayed on the display 100.The microphone 102 can be configured to generate an audio signal thatcorresponds to sound generated by and/or proximate to the patient 12,such as the voice of the patient 12 or the voice of the healthcareprovider 14. The audio signal can be processed by the processor 92 or bythe fourth component 24. Such audio signals can be correlated to anyvideo recording. The speaker 104 can be configured to emit sounds,messages, information, and any other audio signal to the patient 12 orthe healthcare provider 14.

The biometric sensor 106 can be utilized to verify the identity of thehealthcare provider 14 or the patient 12. The cameras 108, 110 can beconfigured to generate a video signal. The cameras 108, 110 can beoperable to capture single images and/or video and to generate a videosignal based thereon. The video signal can include images associatedwith the patient 12 or the healthcare provider 14. The outlet port 112can allow the extraction of data stored in memory 94. The configurationof the outlet port 112 can be selected as desired. By way of example andnot limitation, the outlet port 112 can be a USB-A, USB-B, Mini-A &Mini-B, Micro-B, or Micro-AB.

FIG. 10 is a perspective view of a carousel 116 for a plurality of thirdcomponents 22. Each of the third components 22 can be stored in a slotof the carousel 116. The carousel 116 can include one or more lockingmechanisms to retain the third components 22. Each lock can beselectively unlocked through the use of a biometric sensor. Eachhealthcare provider 14 can engage the particular biometric sensor torelease his/her third component 22.

FIG. 11 is a perspective view of a third component 22 positioned in adocking station 118 outside of the examination room 16. The thirdcomponent 22 can be placed in the docking station 118 and havedownloaded the EHR of the patient 12 in the examination room 16. The LED98 can be blinking to advise the healthcare provider 14 that theexamination can begin.

Clinical data can be directly input via diagnostic instruments into theEHR wirelessly via the information technology system during the patientvisit. FIG. 12 is a diagram of a computing system including an exemplarycomputing device 10 according to some implementations of the presentdisclosure. The components 18, 22, 24 can wirelessly communicate withthe component 24 over a local network 120. The component 24 canwirelessly communicate with a fifth component 122 (another server) overa network 124. As used herein, the term “network” can include, but isnot limited to, a Local Area Network (LAN), a Metropolitan Area Network(MAN), a Wide Area Network (WAN), the Internet, or combinations thereof.Embodiments of the present disclosure can be practiced with a wirelessnetwork, a hard-wired network, or any combination thereof.

FIG. 13 is a screen shot of an exemplary graphic user interface (GUI)according to some implementations of the present disclosure, which canbe displayed on the display 100. The content of the GUI 126 can includea portion for the display of the face of the patient 12, acquired fromthe EHR. This can allow the healthcare provider to confirm the identityof the patient 12. The GUI 126 can also display/confirm the identity ofthe healthcare provider 14. The GUI 126 can provide a plurality of inputoptions for the entry of data defining the medical state of the patient12. The GUI 126 can provide pull-down menus to ensure data is capturedin a controlled format.

In one or more embodiments of the present disclosure, proximity of thepatient's headset (the first component 18) to an interface of adiagnostic instrument (such as second component 20 or third component22) can be utilized to collect and transmit information. This can befurther used to verify that the information is being a linked to thecorrect patient by confirming that the unique identifying numberassociated with the headset close to the instrument matches the numberon the tag linked to the EHR entry in the computer being used to compilethat patient's visit record. As there is no longer a paper chart thattravels with the patient through the office visit, the presence of thephysical first component 18 helps to ensure that no misidentificationoccurs and yet maintains privacy and confidentiality.

As shown in FIG. 1 , the patient 12 can be greeted by a doctor 14 andconducted to an examination or “pre-test” room 16 for a medicalinterview. The purpose of visit can be confirmed. The purpose of thevisit can direct the medical interview. Depending on the requiredcomplexity suggested by visit purpose, the patient 12 can be asked aseries of questions which are answered by selecting from prescribedresponse options. In this fashion, the components of a current medicalstatgraph, which includes a contemporary patient narrative (hereafter“CPN”) is constructed. The technician can serve as a translator toassist the patient 12 in communicating their narrative within theframework of the EHR system.

In one or more embodiments of the present disclosure, patients who donot require face-to-face assistance may complete the CPN via a computerbased interview in a secure area of the practice, and may electronicallyauthorize importation of information from insurers, pharmacies and otherproviders. A keyboard/monitor interface can be used to record thecommunication and understanding of preliminary information such asnotice of privacy practices, informed consent materials, as well asother policies. A signature pad or other biometric reader can be used torecord authorizations and consents. Additionally, some pre-screening andvisual tests (such as color perception) can be presented and performedvia the monitor and a keyboard/mouse/joystick interface.

In response to the nature of the information captured within the CPN,computing device 10 can provide prompts to the healthcare provider 14that tailor the steps of the rest of the medical encounter: technicianexam, refractive services, same visit diagnostic tests, and doctor examcomponents.

A detail example is provided below. A specific question structure anddata options for an ophthalmology practice are described in tables setforth below and can guide history taking and examination, however thepresent disclosure is not limited to ophthalmology practices. Inaddition to CPN data, other categories of data can be collected. Thedata can include observed functional loss (hereafter “OFL”). The datacan include direct examination findings (hereafter “DEF”). OFL positivesor items can be constructed by exams conducted by a Technician andentered into prescribed data fields. DEF positives can be constructedusing initial steps by a technician depending on level of skill, as wellas subsequent steps by the doctor. Positives in CPN, OFL and DEF arelisted and identified by nature, sub-nature and detail descriptorsaccording to positives or deviations from what is expected or normal.The detail descriptors for each category can be of the following types:sub-nature, location, chronological correlations (onset, pattern,cycle), therapeutic correlations, environmental correlations, andfunctional adaptation correlations.

CPN is a subjective, patient reported description of psychic burden,pain and suffering. The categories of CPN can be aspects of subjectivefunctional loss, aspects of sensorimotor anomaly, aspects of pain anddiscomfort, aspects of patient 12 observed abnormalities in appearanceand anatomy, and aspects of history of trauma or past environmentalexposure presenting an increased probability of injury, disease orreduced function. CPN positive items can be gathered as packetsconsisting of a core element describing the essential nature andsub-nature of the item, associated with a measure of severity oramplitude/magnitude and a group of detail descriptors providingdescriptions relating to factors reported to have some correlation withthe magnitude of the CPN item. Data associate with an exemplary CPN ofan exemplary patient 12 is set forth in the table below:

The term “description” can be text entered by a doctor/technician orselected from a pull-down menu. The nature can be defined by a reportedfunctional loss (hereafter “RFL”), algia (physical pain, discomfort orirritation), an SMA, and/or a patient observed abnormality (hereafter“OBAB”). A sensorimotor anomaly is an abstract disorder of sensation orvoluntary movement.

OFL positive items are gathered as packets consisting of a core elementdescribing the essential nature and sub-nature of the item, connected toa measure of severity or amplitude/magnitude. The categories of OFL arerelated to the system under study. In ophthalmology it could be dividedin visual acuity, visual field, and other measures of richness of visionsuch as color perception, contrast sensitivity, and depth perception.Data associated with an exemplary OFL of an exemplary patient is setforth in the table below:

TABLE 2 OFL LOCATION DESCRIPTION SEVERITY NATURE DESCRIPTION SUB-NATUREMAGNITUDE OFL

DEF positive items are gathered as packets consisting of a core elementdescribing the essential nature and sub-nature of the item, as well as adescription of observed pathophysiology connected to a measure ofseverity or amplitude/magnitude. The categories of DEF are associatedwith anatomic location, classified based on observed pathophysiologyfindings and can, for example be labeled with attributes of increased ordecreased presence, size, pigment, movement, circulatory supply, and/orinflammation. Data associate with an exemplary DEF of an exemplarypatient 12 is set forth in the table below:

TABLE 3 DEF LOCATION DESCRIPTION SEVERITY NATURE DESCRIPTION SUB-NATUREPATHOPHYSIOLOGY MAGNITUDE DEF

As set forth above, the present disclosure provides a method of managingan electronic health record and displaying the medical state of thepatient 12 to the health care provider 14. The computing device 10,having one or more processors, can receive a first input. The firstinput can correspond to a first medical state of the patient 12 andinclude a first plurality of positives. Each of the plurality ofpositives can correspond to a deviation from a healthy state for aparticular anatomical portion of the body of the patient and can be anumerical value.

The following table shows a set of data that can define an exemplaryfirst input:

TABLE 4 A B C D E F G H I 1 Information elicited and input into EHR 2 31 Subjective: 4 1a Reported function impairment of biological systemaffected by the disease (Deficit in function) 5 Acute symptom

6 Floaters, left eye 7 CPN SPECIFIC (OCULAR) MSM SYMPTOMATIC) Severitygrade 8 LOCATION DESCRIPTION 9 NATURE DESCRIPTION TYPE COMPLICATIONSDYNAMIC SOB 10 RFL SCOTOMA

ISLAND NA NEW OCCURRENCE VISUAL FIELD 6 11 12 Acute symptom 2 13Photopsia, left eye 14 CPN SPECIFIC (OCULAR) MSN (SYMPTOMATIC} 15LOCATION DESCRIPTION 16 INATURE DESCRIPTION TYPE COMPLICATIONS . DYNAMICSOB 17 REL DYSPHOTOPSIA-SPECTR

NA NEW OCCURRENCE VISUAL FIELD 6 18 19 Chronic symptom

20  Blurred vision for distance, both eyes. R.more than L 21 MSN{SYMPTOMATIC) 22 CPN SPECIFIC (OCULAR) 23 LOCATION DESCRIFTION. 24NATURE DESCRIPTION TYPE COMPLICATIONS DYNAMIC SOB 25 RFC BLURFIXATION-FAR NA NEW OCCURRENCE VISUAL FIELD. 4 26 27 Chronic symptom 228  Blurred vision for near, both eyes. R. more than L 29 CPN SPECIFIC(OCULAR) MSN (SYMPTOMATIC) 30 LOCATION DESCRIPTION. 31 NATUREDESCRIPTION TYPE COMPLICATIONS DYNAMIC SOB 32 RFL BLUR FIXATION FAR NANEW OCCURRENCE VISUAL FIELD 4

indicates data missing or illegible when filed

Names of positives are set forth in cells B6, B13, B20, and B20; thesenames could have been selected from pull-down menus. The data set forthin cells below “Nature Description,” “Type,” “Complications,” “Dynamic,”“Location Description,” and “Severity Grade” for each positive couldhave been selected from pull-down menus. Alternatively, in one or moreembodiments of the present disclosure, the data in one or more of thecells could have been selected or suggested by the computing device 10based on the data selected for other cells. For example, the “SeverityGrade” can be determined by the computing device 10 based on theselection made for another of the categories. The computing device 10can update the EHR of the patient 12 by adding the first input. The EHRcan be stored in the memory associated with the fourth component(server) 24 or the server 122.

The computing device 10 can be compare the first plurality of positiveswith a plurality of different medical conditions each defined by arespective second plurality of positives. For example, the fourthcomponent 24 or the server 122 can include memory storing a database ofdifferent medical conditions. Each medical condition can be defined by aplurality of positives. The positives found in the patient 12 can becompared with positives for each medical condition stored in thedatabase.

The computing device 10 can group at least some of the first pluralityof positives that collectively correlate to at least one of theplurality of different medical conditions and the respective secondplurality of positives. For example, seven positives may be found forthe patient 12. Four of those positives may also be positives of a firstmedical condition. The computing device 10 can group these fourpositives together, defining a problem bundle. One of the positives maybe a positive of a second medical condition. The correlation between apositive and a medical condition can be historically recognized asstrong and the single positive could also define a problem bundle. Oneor more positives may be positives of more than one medical conditionand thus be part of more than one problem bundle. Each problem bundlecan correspond to a particular medical condition.

The following table shows an exemplary set of problem bundles that canbe identified in response to the exemplary first input:

TABLE 5

                                                                 

indicates data missing or illegible when filed

Table 5 is an extension of Table 4. Thus, the computing device 10 canhave determined that thirteen different problem bundles can be relevantto the patient 12 based on the positives found by examination. It isalso noted that the severity grade can be allocated to one problembundle or more than one problem bundle. In row thirty-two, the severitygrad of the positive was selected to be equal to four and was allocated,by the computing device 10, among problem bundles in columns J, K, L,and O.

The computing device 10 can also assess the consistency among theproblem bundles. The following table shows the inclusion of a column fora “mismatch” associated with problem bundles:

TABLE 6 S T U V W X 10 11 12 13 1 Uncorrected Diagnosis of Diagnosis ofDisgnosis of 2 refractive Rheumatoid Cardiovascular hypercholes- 3error, mild arthritis, disease: terolemia, 4 typucal (hypertension),mild 5 typical combined total mismatch 6 7 8 9 0.00 0.00 0.00 0.00 8.000.00 10 11 12 13 14 15 16 0.00 0.00 0.00 0.00 5.00 0.00 17 18 19 20 2122 23 24 1.00 0.00 0.00 0.00 4.00 0.00 25 26 27 28 29 30 31 0.00 0.000.00 0,00 4.00 0.00 32

Table 6 is an extension of Tables 4 and 5. Zeros are set forth in theexemplary mismatch column, representing no mismatch, but for somepositives a number other than zero could be set forth in the mismatchcolumn. For example, the thirteen exemplary problem bundles set forth inTables 4-6 (row 2, columns J-V) can be determined by the computingdevice 10 to be the “most likely problem bundles” or MLPBS applicable tothe patient 12. Although not shown in the Tables 4-6, other positivescould be applicable to the patient, but not be associated with any ofthe MLPBS. The patient 12 may allege other positives or other positivescould be identified during examination that are not associated with anyof the MLPBS. For such positives, the mismatch column would contain thatsame value as the value selected for the severity grade.

A mismatch can be assessed by the computing device 10 in severaldifferent ways. In one or more embodiments of the present disclosure,the computing device 10 can emit a message to the healthcare provider 14in response to the indication of a mismatch. The healthcare provider 14can reassess a positive associated with a mismatch. In one or moreembodiments of the present disclosure, the computing device 10 canre-determine the MLPBS in response to the indication of a mismatch. TheMLPBS can be selected so that the value of one mismatch or allmismatches are minimized. In one or more embodiments of the presentdisclosure, the computing device 10 can store in memory the combinationof positives for further assessment in response to the indication of amismatch. As data associated with more patients is collected, a new orrevised problem bundle can be determined if a particular mismatch isrepeatedly identified. The computing device 10 can thus engage inmachine learning in response to the indication of a mismatch.

The computing device 10 can also determine MLPBS in response to ratiosof the severity grades of various positives. For example, the computingdevice 10 can determine MLPBS in response to the ratio of severitygrades of OFL positives to the severity grades of DEF positives.Alternatively, the computing device 10 can determine MLPBS in responseto the ratio of severity grades of RFL positives to the severity gradesof DEF positives. OFL positives can be less subjective and can thereforebe given greater weight in some circumstances.

The positives elicited at the examination and interview with the patient12 can be displayed by the display 100 or another display controlled bythe computing device 10. The computing device 10 can control a displayto display a plurality of objects. One of the displayed objects can be amedical statgraph. A medical statgraph is shown in FIG. 14 andreferenced at 128. The medical statgraph 128 can be a planar, twodimensional representation of a human body or “homunculus” withsurrounding “pockets” and circumferential “belts.” The medical statgraphis also referred to herein as a homunculus plane. The homunculus plane128 can be divided into a grid of a plurality of cells and wherein atleast some of the plurality of cells correspond to the particularanatomical portions of the body of the patient 12. The variouscross-hatching patterns correspond to different colors that the cellsmay be colored. A homunculus portion 130 (shown with a first shadingpattern) itself can represent an area for placing and recording theanatomical location of DEF components. The pockets (shown with a firstshading pattern) referenced at 132 can represent areas for describingRFL components. The successive layers of belts around the homunculusportion 130 represent areas for grouping and placement of othercomponents of the medical statgraph 128 such as OBAB, OFL, algia, andtrauma. Each cell on the two dimensional grid of the current medicalstatgraph 128 has an x, and y coordinate corresponding to the sub-natureand natures of a particular component of the medical statgraph 128.

In response to the existence of a positive, the computing device 10 willcause the display to display a node on the homunculus plane 128.Exemplary nodes are referenced in FIGS. 15 and 16 at 136, 138, 140, 150,and 152. At least one of the nodes will project away from the homunculusplane 128, from a cell that corresponds to the respective particularanatomical portion of the body of the patient 12. Each node will projectaway from the homunculus plane 128 a height proportional to themagnitude or severity of the positive. A positive having a severitygrade of “6” will have a greater height than a having a severity gradeof “5,” “4,” “3,” etc.

Each node can define a respective cross-section in a plane parallel tothe homunculus plane 128. The nodes 136 and 140 have a rectangularcross-section and node 138 has a circular cross-section. The shape ofthe cross-sections can be selected to convey information. For example,nodes having a rectangular cross-section can represent positivesmeasured objectively and nodes having a circular cross-section canrepresent positives that are defined subjectively, such as node 136.Also, the nodes can be displayed in different colors to conveyinformation. The respective nodes can also be displayed with differentopacities, such as node 136.

FIG. 15 is a perspective view of the homunculus plane 128 shown in FIG.14 , with nodes projecting from the homunculus plane 128 and axesextending between the nodes. The computing device 10 can control adisplay to display an image such as shown in FIG. 15 . FIG. 16 is amagnified portion of FIG. 15 . The nodes are also shown in perspectiveview. Each of the axes interconnect at least two of the nodes.

The nodes within DEF, OFL, and CPN portions of the homunculus plane 128will be displayed over the point or cell in the homunculus plane 128used to describe their nature and sub-nature. Each such point or cellwill have a link or “arrow” to an area or “bubble” which contains adetail descriptor of information in textual form.

Nodes in medical statgraph 10 can be linked by axes to enhance theclarity of problem bundles. Nodes or cells are overlay areas of thehomunculus (human figure and its satellite regions) with the regionsbeing juxtaposed to provide easy visualization of common and importantpatterns of diseases. The first two dimensions of nodes can be locatedon a two-dimensional map whose axes give information about (1)pathophysiological nature and sub-nature of the component element and(2) abstract or anatomical location of the component element. The heightof the node in the third dimension represents the magnitude of thecomponent element identified by the first two dimensions. Severity,extent, risk, acuteness and recalcitrance are examples of measures of“magnitude” for medical purposes and the elements of CPN, OFL and DEFcan all be measured using standardized scales based on largepopulations. The node thus identified in three dimensions in relation toa homunculus, can be given additional attributes described by shape,size, color or luminosity to identify more information of the node suchas correlations of chronology, therapy, environment, and/or functionaladaptation.

The descriptors of the nodes in the first two axes can be classified andarranged to provide the maximum utility with minimum complexity formedical purposes for ease of visualize navigation. While each specialtywill have its own combination of such components, examples suitable forophthalmology have been given but can be generally adapted as they arederived from “first principals”.

The logical connections between positive items linking problem bundleswill form three dimensional shapes—whose conformity with establishedlibraries serve to confirm the correctness of diagnoses and therapeuticplans. The construction of lines represents a method of demonstratingclinical decision making and inferences. The clinician can sort andarrange positives elicited in CPN, OFL and DEF into problem bundles in aprocess designed to be universally applicable and potentially allinclusive. The problem bundles can be arranged to coalesce duplicates,to filter “noise” and remove irrelevant items, and to succinctlycommunicate clinical thought and inferences to reviewers. The cliniciancan seek to link positive items into associated groups and create alogical bridge to therapy plans.

The layout of the homunculus may be fashioned so that easily cognizableinformation is conveyed by the placement, location and orientation ofthe shapes formed by the nodes and intersections comprising the mosttypical problem bundles. The distance of the nodes from a given planemay represent the severity so that incongruous elements are easilyvisible. The composition of the background homunculus may be engineeredso that in the most common disease manifestations, the orientation ofthe intersecting lines between nodes is closest to linear. Similarly theeffect of successful therapy can be visually confirmed by observing themovement of the problem bundle constituents towards the plane from whichdistance corresponds to severity.

Problem bundles can be arranged on and around a representation of thehomunculus for ease of cognitive association and analysis. Thehomunculus is laid out to easily demonstrate disease process linked toobservable changes at an anatomic location or abnormalities ofbiological functional systems associated with or conceptually juxtaposedwith an anatomic location. A problem bundle can be created by groupingfindings in one perspective type (CPN, OFL, or DEF) with findings in asecond perspective type, and then with positives in a third perspectivetype. Problem bundles may consist of only one perspective type but onlyif no matching findings are discovered. A desirable approach can be toattempt to minimize the number of overall problem bundles by associatingas many findings as possible.

The homunculus portion of the homunculus plane can be a two dimensionalhuman figure for concrete anatomic descriptors and surrounding satelliteregions representing functional and subjective elements of the medicalstatus. The regions of the homunculus describe and group predefinedvalues or potential values classified accordingly to their utility inanalyzing and managing disease conditions and are arranged in regions orperspectives, further arranged into categories each of which hasdescriptors.

In one or more embodiments of the present disclosure, a database caninclude a pre-existing library of known disease conditions and theiraccompanying manifestations and symptoms and the EHR can attempt to linkthe positive elements of such conditions into problem bundles by joiningthe elements and connecting by lines. The color and thickness of theconnecting line(s) can indicate the nature of a relationship such as“caused by”, “secondary to”, “associated with” etc.

The two dimensional orientation of the homunculus and its belts can beconfigured so that the most commonly associated elements for the mostimportant disease processes fall in a straight linear path, with theline being parallel to the plane of the homunculus. The further the lineis from the two-dimensional plane of the medical statgraph 10 along thez axis, the greater the overall severity of the problem bundle can beand hence its underlying disease conditions.

A clinician can be prompted to accept or reject suggested problembundles. If proximate problem bundles do not exist in a standard problembundle library, the clinician can be prompted to compile a customproblem bundle. Searches can be made through past medical statgraphs tocheck whether any current problem bundle has occurred before, in whichcase current problem bundle becomes “avatars” of these previouslyidentified problem bundles. Otherwise, they are identified as newproblem bundles.

For new problem bundles, the EHR can prompt the doctor with suggestedtherapy plans based on a library of therapy plans of ascending intensitymatched to appropriate level of severity (or recalcitrance forpre-existing problem bundles). Several alternative options can existwithin each intensity level based on patient idiosyncrasies such age,gender, ethnicity, allergy, formulary coverage etc. with the most costeffective and best matched therapy being presented first. After atherapy plan is activated by the clinician, tasks can be generated forstaff by activation of therapy plan. Examples of action items in atherapy plan can be patient education information and instructionsprinted, prescriptions for medical drugs or devices generated andtransmitted (printed, E-prescribed or E-fax), follow up appointmentsscheduled, additional tests and procedures scheduled and preliminarytasks for these services initiated (such as patient instructions,insurance pre-authorization, informed consent forms, operative reportforms, test interpretation and report forms), correspondence and reportsgenerated for other care-givers and insurance companies, medicalrestriction, FMLA and other paperwork generated for patient asnecessary.

Problem bundles can be formed from the linkage of positive items in CPN,OFL and DEF. Positives are placed as points or nodes over a twodimensional figure to describe the nature, sub-nature and location ofthe items. The purpose of problem bundles is to define disease states(abnormal medical conditions and pathology) as collections of data itemssorted into logical arrays and dimensions with components having natureand sub-nature described. The severity or magnitude of each item may berepresented by intensity or location in the z axis.

Problem bundles can be visually defined by nodes or cells havingrespective heights connected by axes. Disease states derived from themedical consultation are formed into conglomerates termed problembundles. Positive findings from areas of the history and of theexamination are displayed and flagged as unanalyzed data for thesupervising physician. These positive items can be elicited whiledetermining the medical status and can be displayed as nodes or cellsarranged in a three dimensional frame. Nodes are then connected to formproblem bundles. The action of linking positive findings into problembundles can be performed by the supervising physician who can beprompted to create links between positive items. Linkage patterns may besuggested by the system based on existing libraries of typicallyassociated findings and the clinician may accept these suggestions orformulate their own linkage.

Problem bundles can be formed from the grouping of nodes. Excess nodes(those remaining unconnected) can be ignored with respect to activeprocessing and archived for reference in case there are subsequentqueries about the veracity of the diagnoses reached. For example, iftherapy plans do not result in a projected medical status improvements.

FIG. 15 is sample three-dimensional representation of an exemplarymedical statgraph of a patient with two ocular problems, each if whichis connected with a systemic problem. FIG. 16 is a magnified portion ofFIG. 15 . In the example shown in FIG. 15 , the patient has twoophthalmic problem bundles: (1) left cataract, associated with diabetesand (2) dry eye. More specifically, for the first condition, the patienthas a nuclear sclerotic cataract of the left lens evidenced by thefollowing positives:

-   -   CPN, RFL: blur at fixation for distance, blur fixation at near,        dysphotopsia, desaturation, scotoma;    -   OFL: Snellen visual acuity reduced; and    -   DEF: slit lamp examination shows nuclear lenticular opacity.    -   For the second condition, the patient has dry eye on the left        side evidenced by the following positives:    -   CPN, RFL: blur at fixation for distance, blur fixation at near,        and dysphotopsia;    -   CPN, Algia: foreign body sensation (grittiness) of eye;    -   OFL: Snellen visual acuity reduced; and    -   DEF: slit lamp examination shows reduced tear film break up        time.

Referring now to FIG. 17 , the computing device 10 can also control thedisplay to display a planar bundling object 134 in perspective view. Theplanar bundling object 134 can envelope at least some of the firstplurality of nodes. The exemplary planar bundling object 134 envelopesthe nodes 136, 138, as well as other nodes. By enveloping, the planarbundling object 134 serves to wrap or cover, or to surround a group ofnodes. The planar bundling object 134, in various embodiments, can betransverse and parallel to the homunculus plane 128 and at leastpartially spaced from the homunculus plane 128.

The exemplary planar bundling object 134 is a linear segmentcircumscribing at least some of the first plurality of nodes. In one ormore other embodiments of the present disclosure, the planar bundlingobject can be a plane of solid color intersecting at least some of thefirst plurality of nodes. FIG. 18 is a side view of the objects shown inperspective view in FIG. 17 . FIG. 18 also includes a second planarbundling object 146. The computing device 10 can allow the healthcareprovider 14 to orient the displayed objects as desired, including sideviews such as FIG. 18 where planar bundling objects can be viewed as aline and perspective views such as FIGS. 15-17 . 14.

In FIG. 18 , the second planar bundling object 146 can be plane that isarranged to at least partially intersect all of the nodes. Thehealthcare provider 14 can control the computing device 10 to change thedisplay to orient the second planar bundling object 146 in perspectiveview. The angle between the second planar bundling object 146 and thehomunculus plane 128 can convey information about the medical state ofthe patient 12 to the healthcare provider 14. For example, if all of thenodes are at substantially similar height, the second planar bundlingobject 146 and the homunculus plane 128 will be substantially parallelto one another. This can indicate a greater likelihood that theassociated problem bundle is a condition suffered by the patient. On theother hand, if all of the nodes are at various heights, the secondplanar bundling object 146 (intersecting all of the nodes) will beskewed relative to the homunculus plane 128. This can indicate a lowerlikelihood that the associated problem bundle is the condition sufferedby the patient.

These attributes of the display can also be extremely useful in trackingthe efficacy of a course of treatment. For example, the computing device10 can retrieve, from the electronic health record of the patient thatis stored in memory, a second input. The second input can correspond toa prior medical state of the patient. The second input can include aplurality of positives, similar to the first input. Each of thepositives corresponds to a deviation from a healthy state for aparticular anatomical portion of the patient's body and is a numericalvalue, similar to the positives of the first input. It is noted that, asused herein, a particular anatomical portion of the patient's bodycorresponds to a physical anatomic location or its associated “belt”linking function on the homunculus plane 128 or subjective symptomsassociated with that location.

The computing device 10 can display nodes corresponding to the positivesof the second input concurrently with the nodes corresponding to thepositives of the first input. Each of the nodes associated with thesecond input extend a height above the homunculus plane 128, similar tothe nodes of the first input. The nodes associated with the second inputcan be overlaid on the nodes associated with the first input. Therespective nodes can be differently colored and/or have differentopacities. This will allow the healthcare provider 14 to readily see ifthe medical state of the patient 12 has improved or worsened. If thenodes associated with the first input are taller than the nodesassociated with the second input, the medical state of the patient 12has improved. If, however, the nodes associated with the first input areshorter than the nodes associated with the second input, the medicalstate of the patient 12 has worsened. Similarly, planar bundling objectsgenerated at different times can be compared to quickly and readilyassess changes in the condition of the patient 12.

While the present disclosure has been described with reference to anexemplary embodiment, it will be understood by those skilled in the artthat various changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the presentdisclosure. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the presentdisclosure without departing from the essential scope thereof.Therefore, it is intended that the present disclosure not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this present disclosure, but that the present disclosurewill include all embodiments falling within the scope of the appendedclaims. Further, the “present disclosure” as that term is used in thisdocument is what is claimed in the claims of this document. The right toclaim elements and/or sub-combinations that are disclosed herein asother present disclosures in other patent documents is herebyunconditionally reserved.

1. A method for applying machine learning in displaying a patient's medical state comprising: receiving, at a computing device having one or more processors, a first input of a first set of data corresponding to a first medical state of the patient and including a first plurality of positives, wherein each of the first plurality of positives corresponds to a deviation from a healthy state for a particular anatomical portion of the patient's body and is a numerical value; grouping, at the computing device, at least some of the first plurality of positives to a second plurality of positives wherein a plurality of different medical conditions are each defined by a respective second data set of one of more the second plurality of positives, wherein the at least some of the first plurality of positives collectively correlate to at least one of the plurality of different medical conditions and the respective second plurality of positives, thus defining a first plurality a problem bundles associated with the patient wherein each problem bundle is at least one of the first plurality of positives of the first set of data grouped to at least one of the second plurality of positives of the second set of data of a particular medical condition; identifying, at the computing device, at least one mismatch with the first plurality of problem bundles associated with the patient during said grouping, wherein said at least one mismatch is at least one of the first plurality of positives of the patient that is not groupable to one of the second plurality of positives of at least one particular medical condition, said at least one of the first plurality of positives thus in the first set of data and not in the second set of data of the at least one particular medical condition; displaying, on a display controlled by the computing device, a first statgraph that includes a plurality of objects including a first plurality of nodes each corresponding to one of the first plurality of positives; reducing mismatches, at the computing device, via a machine learning algorithm, including: repeatedly identifying, at the computing device, a first mismatch between a first positive and a first problem bundle involving a first medical condition, and revising, at the computing device, in response to said repeatedly identifying, a second data set of the first medical condition to include the first positive and thereby eliminate the first mismatch from a subsequent grouping involving the first medical condition.
 2. The method of claim 1 wherein said displaying step is further defined as: displaying the first statgraph to include the plurality of objects, wherein the plurality of objects also include: a homunculus plane in perspective view, wherein each of the first plurality of nodes is displayed in perspective view and each projects away from the homunculus plane at a location that corresponds to a particular anatomical portion of the patient's body, wherein a height of each of the first plurality of nodes from the homunculus plane corresponds to the respective numerical value, and a first plurality of axes each interconnecting at least two of the first plurality of nodes.
 3. The method of claim 2 further comprising: receiving, at the computing device, a second input corresponding to a second medical state of the patient and including a third plurality of positives, wherein each of the third plurality of positives corresponds to a deviation from a healthy state for a particular anatomical portion of the patient's body and is a numerical value.
 4. The method of claim 3 further comprising: grouping, at the computing device, at least some of the third plurality of positives to a fourth plurality of positives wherein a plurality of different medical conditions are defined by one of more the fourth plurality of positives, wherein the at least some of the third plurality of positives collectively correlate to at least one of the plurality of different medical conditions and the respective fourth plurality of positives.
 5. The method of claim 4 further comprising: morphing, on the display controlled by the computing device, the displayed first statgraph into a second statgraph that includes: the homunculus plane in perspective view, a second plurality of nodes each in perspective view, each corresponding to one of the third plurality of positives, and each projecting away from the homunculus plane at the cell that corresponds to the respective particular anatomical portion of the patient's body, wherein a height of each of the second plurality of nodes from the homunculus plane corresponds to the respective numerical value, the first plurality of nodes morphed into the second plurality of nodes, and a second plurality of axes each interconnecting at least two of the second plurality of nodes, the first plurality of axes morphed into the second plurality of axes.
 6. The method of claim 2 wherein each of the first plurality of nodes is displayed in a respective color and wherein at least two of the first plurality of nodes are displayed with different colors.
 7. The method of claim 2 wherein each of the nodes is displayed with a respective opacity and wherein at least two of the first plurality of nodes are displayed with different opacities.
 8. The method of claim 2 further comprising: controlling the display, with the computing device, to change the viewing perspective to a side view such that the homunculus plane is displayed as a line.
 9. A system for generating a display of a patient's medical state based on machine learning and comprising: a display; and a computing device, comprising one or more processors and a non-transitory, computer readable medium storing instructions that, when executed by the one or more processors, cause the computing device to perform operations comprising: receiving, at a computing device having one or more processors, a first input of a first set of data corresponding to a first medical state of the patient and including a first plurality of positives, wherein each of the first plurality of positives corresponds to a deviation from a healthy state for a particular anatomical portion of the patient's body and is a numerical value; grouping, at the computing device, at least some of the first plurality of positives to a second plurality of positives wherein a plurality of different medical conditions are each defined by a respective second data set of one of more the second plurality of positives, wherein the at least some of the first plurality of positives collectively correlate to at least one of the plurality of different medical conditions and the respective second plurality of positives, thus defining a first plurality a problem bundles associated with the patient wherein each problem bundle is at least one of the first plurality of positives of the first set of data grouped to at least one of the second plurality of positives of the second set of data of a particular medical condition; identifying, at the computing device, at least one mismatch with the first plurality of problem bundles associated with the patient during said grouping, wherein said at least one mismatch is at least one of the first plurality of positives of the patient that is not groupable to one of the second plurality of positives of at least one particular medical condition, said at least one of the first plurality of positives thus in the first set of data and not in the second set of data of the at least one particular medical condition; displaying, on the display, a plurality of objects including a first plurality of nodes each corresponding to one of the first plurality of positives; and reducing mismatches, at the computing device, via a machine learning algorithm, including: repeatedly identifying, at the computing device, a first mismatch between a first positive and a first problem bundle involving a first medical condition, and revising, at the computing device, in response to said repeatedly identifying, a second data set of the first medical condition to include the first positive and thereby eliminate the first mismatch from a subsequent grouping involving the first medical condition.
 10. The system of claim 9 wherein said computing device is further defined as comprising: a first component being a device wearable by the patient, said first component including transceiver, a speaker, and a microphone.
 11. The system of claim 9 wherein said computing device is further defined as comprising: a second component being a stylus including one or more transducers and a transceiver.
 12. The system of claim 9 wherein said computing device is further defined as comprising: a third component being a tablet including a transceiver and wherein said display is mounted in said tablet.
 13. A computer program product comprising program code stored on a non-transitory computer-readable medium, which when executed by a computing device having one or more processors, enables the computing device to apply machine learning in displaying a patient's medical state by performing actions including: receiving, at a computing device having one or more processors, a first input of a first set of data corresponding to a first medical state of the patient and including a first plurality of positives, wherein each of the first plurality of positives corresponds to a deviation from a healthy state for a particular anatomical portion of the patient's body and is a numerical value; grouping, at the computing device, at least some of the first plurality of positives to a second plurality of positives wherein a plurality of different medical conditions are each defined by a respective second data set of one of more the second plurality of positives, wherein the at least some of the first plurality of positives collectively correlate to at least one of the plurality of different medical conditions and the respective second plurality of positives, thus defining a first plurality a problem bundles associated with the patient wherein each problem bundle is at least one of the first plurality of positives of the first set of data grouped to at least one of the second plurality of positives of the second set of data of a particular medical condition; identifying, at the computing device, at least one mismatch with the first plurality of problem bundles associated with the patient during said grouping, wherein said at least one mismatch is at least one of the first plurality of positives of the patient that is not groupable to one of the second plurality of positives of at least one particular medical condition, said at least one of the first plurality of positives thus in the first set of data and not in the second set of data of the at least one particular medical condition; displaying, on a display controlled by the computing device, a plurality of objects including a first plurality of nodes each corresponding to one of the first plurality of positives; and reducing mismatches, at the computing device, via a machine learning algorithm, including: repeatedly identifying, at the computing device, a first mismatch between a first positive and a first problem bundle involving a first medical condition, and revising, at the computing device, in response to said repeatedly identifying, a second data set of the first medical condition to include the first positive and thereby eliminate the first mismatch from a subsequent grouping involving the first medical condition. 